Journal of Medical Engineering and Technology最新文献

The growing need for efficient patient lifting and transfer solutions highlights a significant gap in current healthcare systems, particularly in affordable, accessible options for home use. While most research has focused on automated or motorised systems, this study introduces a novel manual patient lifting device based on a worm gear mechanism, which, despite its proven industrial benefits, remains underexplored in healthcare. Using a case study of a 50-year-old, 72 kg individual, we developed a cost-effective, manually operated lifting system aimed at reducing caregiver workload and improving patient mobility. The design was modelled using SolidWorks and subjected to comprehensive static and dynamic structural analysis under loads of 800 N, 1000 N and 1200 N. Results show that the worm gear mechanism reduces required torque by up to 66% and applied force by 15% compared to traditional lead screw systems, significantly enhancing ergonomic efficiency. Additionally, lifting speed improves by approximately 10 mm/s, and the device achieves a safety factor of 2.9 under maximum load, ensuring structural reliability. Importantly, the non-back driveable feature of the worm gear prevents unintended descent, addressing a key safety concern in manual lifting devices. This mechanically optimised and ergonomically designed solution is tailored for homecare settings, where affordability, ease of use, and portability are crucial. By applying advanced mechanical principles to a simple, reliable design, this work contributes to the development of practical assistive technologies that improve both caregiver safety and patient independence, marking a meaningful step forward in assistive healthcare technology.

The precise measurement of bite force is vital in dental diagnostics, particularly for evaluating tooth restorations, prosthetic interventions, and orthodontic treatments. This study presents the calibration and evaluation of the Flexiforce A301 sensor using optimised low-drive voltage circuits to extend its measurement range. Three circuit configurations, a voltage divider, a feedback resistor, and a feedback resistor with a capacitor were designed, simulated using LTspice, and experimentally validated. Results indicate that the configuration incorporating a feedback resistor provides superior linearity and stability, accurately measuring forces up to 100 kg, outperforming other configurations. This advancement enhances the reliability and range of bite force measurements, offering a robust foundation for high-force dental applications.

Rehabilitation of transfemoral amputees remains a societal challenge due to the absence of natural knee joint motion. Despite progress in high-end prosthetic knee joints, issues of affordability, functionality, and patient-specific fitting persist. This study addresses these concerns through an indigenously developed, patient-specific configurable polycentric knee joint with improved functionalities. Five transfemoral amputees and ten healthy controls participated. The prosthesis is fitted to amputees, followed by a 12-week rehabilitation program. Pre- and post-fixation assessments are conducted using SF-36 and QTFA-70 to evaluate health-related quality of life (HRQL) and functionality. Kinematic and dynamic analyses during daily activities are performed using a high-speed video camera, Kinovea software, manual goniometer, and force plate. Results show a 55% improvement in HRQL and 88% improvement in global performance post-fixation. The measured knee flexion angles are 47.6°±5.9° (swing phase), 131.4°±6.6° (deep squat), 112.8°±5° (floor sitting), 125.1°±5.4° (chair sitting), and 99.2°±4.5° (bent knee sitting), closely matching healthy controls. Peak vertical ground reaction forces and gait symmetry also align with sound limbs and controls. These outcomes demonstrate the prosthetic design's potential in restoring near-anatomical motion and significantly improving the functional performance of transfemoral amputees.

In the proposed work, strain shielding effect analysis of solid and porous Ti-6Al-4V alloy implanted femur bone using finite element analysis is carried out. Strain shielding is a significant concern during total hip arthroplasty (THA) since it reduces bone growth and results in aseptic implant loosening due to the mismatch of femur and implant characteristics. The study examined solid and porous implanted femur bone under three loading conditions: standing, walking and stair climbing. The results show that strains on bone due to porous implants as compared to solid implants have been increased by 31, 24.3% and reduced by 12.18% for standing, walking, and stair climbing human activities, respectively. The findings show that porous implants promote bone growth and reduce aseptic implant loosening by lowering the strain and stress shielding effect.

Retinal vessel segmentation is essential for precise ophthalmological diagnoses, particularly in the prediction of retinal degenerative diseases. However, existing methods usually lack robustness and accuracy, especially in segmentation of thin or overlapping vessels. To face these challenges, this study introduces an enhanced retina-RV-Gain segmentation model, which employs an architecture of various stages to refine the results of segmentation iteratively. The model integrates attention mechanisms to better capture complex vessel structures and employs an adaptive loss function to manage class imbalance. In addition, a specially designed discriminator enhances the model's ability to distinguish fine details from background noise vessels. The proposed RV-Gan is trained in comprehensive data sets that comprise retinal images, segmentation masks and noted labels, including Stare-DB, Chase-DB1 and Drive, using the Python platform. Experimental results demonstrate a segmentation accuracy of up to 99% in normal, abnormal and base vessels. These findings highlight the potential of the model to significantly improve diagnostic accuracy and support early prediction of disease in clinical ophthalmology. Overall, the enhanced RV-Gan architecture offers a robust solution to the limitations of current approaches, providing segmentation of high fidelity retinal vessels and advancing the predictive analysis of retinal degenerative conditions.

Sleep-disordered breathing (SDB) affects 14% of the population. Positive airway pressure (PAP) therapy is standard, but commercially available interfaces may be ineffective due to poor fit. Three-dimensional (3D) printing can customise PAP therapy interfaces. Is it feasible to manufacture and use 3D-printed customised oronasal PAP interfaces in clinical practice? Do customised interfaces improve patient comfort and reduce side effects compared to off-the-shelf interfaces? A single-site feasibility study involving 10 healthy and 10 patient participants was undertaken. A 3D facial scan was used to 3D print a mould, injected with medical-grade silicone to create a oronasal customised interface. Participants underwent a 10-minute trial with both off-the-shelf and customised interfaces. Comfort (Visual Analogue Scale), skin reactions, and interface leak (L/min) were measured. Patient participants used the customised interface for five nights at home, with data collected on Apnoea Hypopnoea Index (AHI), interface leak, and PAP therapy concordance. The study recruited 20 participants. Customised oronasal interfaces showed a failure rate in manufacturing (23.75% 3D printing, 50%: silicone injection). Adverse reactions were 10% in the patient study. Comfort scores were similar between interfaces. Interface leak was lower with customised interfaces after five nights. AHI was reduced with customised interfaces, but with a trend towards decreased PAP therapy concordance. The study demonstrated 3D-printed customised oronasal PAP interfaces can be manufactured, with potential benefits of reduced interface leak and AHI. Improvements in manufacturing processes are needed to reduce failure rates. Further research via a randomised controlled trial with a longer duration is warranted.

Deep learning's swift development has generated substantial excitement about its application in medical imaging. Machine learning (ML) methods can support radiologists in diagnosing breast cancer (BC) without resorting to invasive procedures. However, traditional ML classifiers require the extraction of detailed hand-crafted features, which is a time-intensive task to achieve accurate results. Hence, this paper proposes a novel Feature-driven Breast Cancer Classification using the Modified Loss and Activation function-assisted LeNet (MLAL) model, named F-BCC-ML. The process of detecting BC using mammogram images comprises several key stages. In the first step, the image undergoes enhancement using the Improved Bilateral Filtering Technique (IBFT), which reduces the noise while conserving critical structural details like edges. Next, the image is subjected to segmentation using SegNet, a deep-learning model designed for semantic segmentation. After segmentation, the next phase is feature extraction, where various features like Weber Local descriptor assisted Local Gabor XOR Pattern (WLD-LGXP) for texture analysis, Median Binary Pattern (MBP), colour features, and deep features are derived from the segmented image. Once the features are extracted, they are fed into the classification stage, where the Modified Loss and Activation function assisted LeNet (MLAL) model, more sophisticated Deep Convolutional Neural Network (DCNN) are used to classify the image as either normal or cancerous. The result is a prediction that indicates whether the breast tissue is benign or shows signs of cancer, helping radiologists make more accurate and informed decisions. The MLAL+DCNN accomplished the maximum accuracy of 0.936, precision of 0.947 and F-measure of 0.942, respectively.

Transcatheter aortic valve implantation (TAVI) is now the standard treatment for aortic stenosis, offering a less invasive alternative to surgery. While 3D printing and finite element analysis (FEA) show promise for pre-procedural planning, their accuracy in predicting post-TAVI device geometry remains unclear. This study evaluates the agreement between patient-specific FEA models, 3D-printed phantoms, and post-TAVI CT imaging in replicating implanted device geometry. Ten patients treated with the SAPIEN 3 Ultra (S3) device were analysed using pre- and post-TAVI CT scans. Both FEA simulations and 3D-printed models were assessed for stent deformation and anatomical fit. Agreement was quantified using statistical tools including concordance correlation coefficient (CCC), intraclass correlation coefficient (ICC), and Bland-Altman plots. FEA showed stronger agreement with post-TAVI CT (ICC = 0.614, CCC = 0.479) than 3D printing (ICC = 0.363, CCC = 0.165), which had higher variability. While FEA closely approximated device expansion at the annular level, both methods had limitations due to material and computational assumptions. The study supports the greater reliability of FEA in pre-procedural planning, highlighting the need for further validation and standardisation.

Capturing limb shape for amputees is critical in the fabrication and delivery of comfortable prosthetic limbs. Smartphone Photogrammetry offers a cheaper and more accessible alternative to digital shape capture than traditional handheld 3D scanners, opening possibilities for remote, or in home scanning. In this study we aimed to evaluate the accuracy of smartphone photogrammetry using a technique designed for in home scanning, comparing performance to an Einscan H2. The results indicated that photogrammetry was suitable accurate for scanning static limb targets (>95% volumetric accuracy), but was not accurate enough for direct amputee scanning (63.4% larger volumes). Whilst this technique was not sufficiently accurate for clinical use, the amputee surrogate trials did show increased accuracy, indicating the method shows promise and should be developed further, with a particular focus on home environment compatible techniques.